Title of article :
Monitoring and assessment of water health quality in the Tajan River, Iran using physicochemical, fish and macroinvertebrates indices
Author/Authors :
Aazami، Jaber نويسنده MSc Student, Faculty of Natural Resources and Marine Science, Tarbiat Modares University, Noor, Iran , , Esmaili Sari، Abbas نويسنده Department of Environment, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran , , Abdoli، Asghar نويسنده Department of Virology, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran , , Sohrabi، Hormoz نويسنده Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran , , Van den Brink، Paul J نويسنده Department of Aquatic Ecology and Water Quality Management, Wageningen University, Wageningen University and Research Centre, Wageningen, The NetherlandsAlterra, Wageningen University and Research Centre, Wageningen, The Netherlands ,
Abstract :
Background: Nowadays, aquatic organisms are used as bio-indicators to assess ecological water quality in western
regions, but have hardly been used in an Iranian context. We, therefore, evaluated the suitability of several indices
to assess the water quality for an Iranian case study.
Methods: Measured data on biotic (fish and macroinvertebrates) and abiotic elements (28 physicochemical and
habitat parameters), were used to calculate six indices for assessment of water quality and the impact of human
activities in the Tajan river, Iran. GIS, uni- and multivariate statistics were used to assess the correlations between
biological and environmental endpoints.
Results: The results showed that ecological condition and water quality were reduced from up- to downstream.
The reduced water quality was revealed by the biotic indices better than the abiotic ones which were linked to a
variety of ecological water quality scales.
Conclusion: The fish index showed a strong relationship with long-term database of physicochemical parameters
(12 years (94%)), whereas macroinvertebrates index is more correlated with short-term data (76%). Meanwhile, the
biotic and abiotic elements in this study were also classified well by PCA. Pulp and wood plants and sand mining
are indicated to have the most negative effects on the river ecosystem.